• Online, Self-Paced
Course Description

The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Explore how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

Learning Objectives

Using Clustering Techniques

  • start the course
  • recognize characteristics of clustering
  • identify the different types of clustering
  • calculate proximity

K-Means Clustering

  • list key features of K-Means Clustering
  • recognize key steps for reducing the sum of squared errors in K-Means Clustering
  • recognize key steps for the termination of K-Means Clustering iterations
  • evaluate K-Means Clustering

Hierarchical Clustering and DBSCAN

  • list key features of Hierarchical Clustering and DBSCAN
  • recognize key steps in DBSCAN
  • identify key attributes for performing DBSCAN

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.